This study explored the use of coir fibers extracted from coconut husks, an agro-waste material that constitutes sanitation and environmental pollution problems, as a reinforcing element in the production of metakaolin-based geopolymer composites with improved properties. A series of sample formulations were produced with varying coir fiber content (0.5, 1.0, 1.5, and 2.0 percent weight of metakaolin powder). The investigation was conducted using a 10 M NaOH alkaline solution with a 0.24 NaOH:Na₂SiO₃ mass ratio. Samples were cured for 28 days and tested for bulk density, ultrasonic pulse velocity (UPV), and compressive and flexural strength. Microstructural examinations such as X-ray diffraction (XRD), Fourier transform infrared (FTIR) spectroscopy, and scanning electron microscopy (SEM) were also performed on samples. Compressive strength values up to 21.25 N/mm² at 0.5% fiber content and flexural strength values up to 10.39 N/mm² at 1% fiber content were achieved in this study. The results obtained showed a decreasing bulk density of geopolymer samples (2113 kg/m³ to 2045 kg/m³) with increasing coir fiber content. The geopolymer samples had UPV values varying from 2315 m/s to 2717 m/s. Coir fiber with 0.5–1.0% fiber content can be incorporated into metakaolin-based geopolymers to produce eco-friendly composite materials with improved mechanical properties for sustainable development.

Various research works have been carried out on cement and lime stabilization of lateritic soil. The production of cement is too expensive and environmentally unfriendly due to the required huge embodied energy as well as the environmental pollution resulting in ozone layer depletion. In order to minimize these shortcomings, researchers are exploring a sustainable alternative to cement. There is limited scholarly work done on the use of a combination of agro-waste ashes for the stabilization of lateritic soil. This study is aimed at examining the performance of lateritic soil stabilized with a combination of bone ash (BA) and palm bunch ash (PBA) for sustainable building applications. Different cube sizes (50 mm³, 70 mm^3, and 100 mm³) were used to produce lateritic bricks of different matrices which were used to obtain the compressive strength test results. Addition of a minimal percentage of BA (2%) and PBA (2%) improved the compressive strength of lateritic bricks by 129% compared to the natural lateritic soil. Brick specimen produced with the smallest mould size (2500 mm²) gave higher compressive strength compared to larger mould size. The findings of this study confirm the potential of using lateritic soil stabilized with a minimal percentage of BA (2%) and PBA (2%) for sustainable building applications.

This paper explores the effects of cement stabilization (5, 10, 15 and 20 wt%) on the structural and mechanical properties (compressive/flexural strengths and fracture toughness) of abandoned termite mound soil. The crystal structures and crystallinity of the constituents were determined using X-ray diffraction (XRD), while the microstructure was characterized via scanning electron microscopy (SEM) and energy dispersive X-ray spectroscopy (EDS). The functional groups were also identified using Fourier transform infra-red spectroscopy (FTIR). The compressive/flexural strengths of the stabilized and un-stabilized termite mound soil were also studied after curing for 7, 14 and 28 days. The fracture toughness mechanism was analyzed with the aid of the R-curve method. Additionally, the underlying deformation and cracking mechanisms are elucidated via in-situ/ex-situ optical and scanning electron microscopy. The stabilized termite mound soil displayed the highest mechanical properties of 13.91 MPa, 10.25 MPa and 3.52 kPa·m^1/2 for compressive strength, flexural strength and fracture toughness, respectively. Besides displaying good mechanical properties and being locally available at no cost, renewable and an eco-friendly material, the termite mound soil will contribute to lowering the cost of housing in Sub-Saharan Africa, particularly in Chad.

This research focuses on the environmental-friendly production of Borassus fruit fibers (BNF), its characterization, and hygroscopic properties determination via Dynamic Vapor Sorption (DVS). The experimental results obtained from the hygroscopic behavior analysis were used to create a primary dataset to train and test Decision Tree Regression (DTR) and Gradient Boosting Regressor (GBR) models. The created primary dataset comprised 294 observations, from which 80% were used to train the models, and the remaining 20% were used for the testing of the two models. The models exhibited high accuracy, easy interpretability on the small-size dataset, and flexibility with regards to the nature of the relationship between the input and output variable. Both models successfully predicted the hygroscopic behavior with the Gradient Boosting Regressor outperforming Decision Tree Regression by indicating values of 0.012, 0.109, 0.059, and 0.999 for MSE, RMSE, MAE, and R², respectively, during the desorption of the BNF, and values of 0.012, 0.109, 0.059, and 0.999 for MSE, RMSE, MAE, and R², respectively, during the desorption of the BNF. This suggests that the Gradient Boosting Regressor illustrated the maximum accuracy. The outcomes can be utilized to provide an alternative for traditional methods, which can often be costly and time-consuming by improving the engineering properties of BNF. The models can be used in the construction sector to lower costs as they are able to pinpoint elements influencing the characteristics for specific applications to grasp its various properties through the prediction of its hygroscopic properties.

This investigation prospects the feasibility of optimizing the mechanical behavior and dimensional stability of termite's mound soil through alkaline activation. The raw aluminosilicate (termites' soil) was used without any pre-thermal treatment and natural occurring potash was used as the alkaline activator. Different activation level and different initial curing temperature were adopted to examine the effect of the initial temperature and the activator concentration on the Alkali Activated Termite Soil (AATS). Similarly, Scanning Electron Microscopy (SEM)/Energy Dispersive X-ray Spectroscopy (EDS), X-ray Diffraction (XRD) and Fourier Transform Infra-Red Spectroscopy (FTIR) were conducted to characterize the microstructure, to determine the crystallinity of the constituents and to identify the functional groups present within the specimens. These characterizations were carried out on the specimens at 15 days after their moulding. The compressive strength was determined for 7, 15 and 90 days to illuminate the fundamental of the optimization process. Results showed that the optimal initial curing temperature was 60°C for the oven-dry regime at 3wt% activator with compressive strength of 2.56, 4.38 and 7.79 MPa at 7, 15 and 90 days respectively. From the mechanical performances results, the alkali stabilized termite's soil can be used as masonry elements predominantly submitted to compression. The repercussions of the results are analyzed for potential applications of the Alkaline Activation techniques as an environmental-friendly approach to obtain renewable and sustainable building materials at low cost with low energy consumption henceforth replicable in most of the regions.

Earth-based materials have shown promise in the development of ecofriendly and sustainable construction materials. However, their unconventional usage in the construction field makes the estimation of their properties difficult and inaccurate. Often, the determination of their properties is conducted based on a conventional materials procedure. Hence, there is inaccuracy in understanding the properties of the unconventional materials. To obtain more accurate properties, a support vector machine (SVM), artificial neural network (ANN) and linear regression (LR) were used to predict the compressive strength of the alkali-activated termite soil. In this study, factors such as activator concentration, Si/Al, initial curing temperature, water absorption, weight and curing regime were used as input parameters due to their significant effect in the compressive strength. The experimental results depict that SVM outperforms ANN and LR in terms of R² score and root mean square error (RMSE).